融入空间关系的GMM全色高分辨率遥感影像监督分割方法

doi:10.11999/JEIT160798

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Abstract

This paper proposes a supervised image segmentation algorithm for high resolution remote sensing images by introducing the Gaussian Mixture Model (GMM) with spatial relationship in order to solve the problem of the increasing dissimilarity in the same object and the decreasing of dissimilarity between two different objects. The proposed algorithm takes samples according to the segmentation areas and uses the least squared method to fit the histogram. GMMs are established to describe the complex spectral characteristic in each area accurately. Then spatial relationships are taken consider into the probability measures in GMMs to make the dissimilarities of pixels in a window is determined by all the pixels in the same window. Overall the GMMs can describe the spatial relationships between the pixels in high resolution remote sensing images. Finally the segmentation result is obtained by maximum probability principle. To verify the feasibility and the effectively of the proposed algorithm, the algorithm is performed on real high resolution remote sensing and synthetic images and compared the results with that of FCM and HMRF-FCM based segmentation algorithm. Qualitative and quantitative results prove that the proposed algorithm could improve the accuracy of segmentation.

Key words： High resolution remote sensing image Gaussian Mixture Model (GMM) Spatial relationship Supervised segmentation

Received: 26 July 2016 Published: 28 February 2017

PACS:

P237

Fund:

The General Science Research Project of Education Bureau of Liaoning Province (LJYL036, LJYL012), The Research Fund for the Doctoral Program of Higher Education of China (20122121110007)

Corresponding Authors: XU Aigong E-mail: xu_ag@126.com

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	WANG Chunyan
	XU Aigong
	SUN Chuan
	ZHAO Xuemei

Cite this article:

WANG Chunyan,XU Aigong,SUN Chuan等. Surpervised Segmentation Algorithm Based on GMM with Spatial Relationship for High Resolution Ranchromatic Remote Sensing Image[J]. JEIT, 2017, 39(5): 1071-1078.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160798 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I5/1071

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